fix

notebooks/minifloat_mx_tutorial.ipynb

@@ -206,15 +206,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Non padding weights shape torch.Size([64, 8, 3, 3])\n",
-      "Padded weights shape torch.Size([64, 32, 3, 3])\n"
+      "Non padding weights shape torch.Size([64, 1, 8, 3, 3])\n",
+      "Padded weights shape torch.Size([64, 1, 32, 3, 3])\n"
      ]
     }
    ],
@@ -257,8 +257,8 @@
     "o = mx_model(x)\n",
     "\n",
     "# The quant weight of the padded model is different from the non padding one\n",
-    "print(f\"Non padding weights shape {mx_model_no_padding.conv.quant_weight().value.shape}\")\n",
-    "print(f\"Padded weights shape {mx_model.conv.quant_weight().value.shape}\")\n",
+    "print(f\"Non padding weights shape {mx_model_no_padding.conv.quant_weight().value_.shape}\")\n",
+    "print(f\"Padded weights shape {mx_model.conv.quant_weight().value_.shape}\")\n",
     "\n",
     "# However, results are still the same \n",
     "assert torch.allclose(o, o_no_padding)"

src/brevitas/graph/gpxq.py

@@ -187,12 +187,6 @@ def __init__(
         self.layer = layer
         self.name = name
         self.act_order = act_order
-        if self.layer.weight_quant.is_groupwise:
-            weight = self.layer.weight_quant.apply_input_view(self.layer.weight)
-            weight = weight.view(self.layer.weight_quant.quant_injector.reshaped_groupwise_shape)
-            self.layer.weight.data = weight.data
-            self.layer.in_channels = weight.shape[1] if is_conv_transposed(
-                self.layer) else weight.shape[0]
 
         weight_shape = torch.tensor(layer.weight.shape)
 

src/brevitas/quant_tensor/groupwise_float_quant_tensor.py

@@ -113,12 +113,12 @@ def expand(self):
 
         if residual > 0:
             new_value = torch.stack(
-                torch.unbind(new_value, dim=self.group_dim)[residual:], dim=self.group_dim)
+                torch.unbind(new_value, dim=self.group_dim)[:unpadding_shape], dim=self.group_dim)
             new_scale = torch.stack(
-                torch.unbind(new_scale, dim=self.group_dim)[residual:], dim=self.group_dim)
+                torch.unbind(new_scale, dim=self.group_dim)[:unpadding_shape], dim=self.group_dim)
             if self.zero_point_.shape != ():
                 new_zp = torch.stack(
-                    torch.unbind(new_zp, dim=self.group_dim)[residual:], dim=self.group_dim)
+                    torch.unbind(new_zp, dim=self.group_dim)[:unpadding_shape], dim=self.group_dim)
 
         return new_value, new_scale, new_zp
 

src/brevitas/quant_tensor/groupwise_int_quant_tensor.py

@@ -99,12 +99,12 @@ def expand(self):
 
         if residual > 0:
             new_value = torch.stack(
-                torch.unbind(new_value, dim=self.group_dim)[residual:], dim=self.group_dim)
+                torch.unbind(new_value, dim=self.group_dim)[:unpadding_shape], dim=self.group_dim)
             new_scale = torch.stack(
-                torch.unbind(new_scale, dim=self.group_dim)[residual:], dim=self.group_dim)
+                torch.unbind(new_scale, dim=self.group_dim)[:unpadding_shape], dim=self.group_dim)
             if self.zero_point_.shape != ():
                 new_zp = torch.stack(
-                    torch.unbind(new_zp, dim=self.group_dim)[residual:], dim=self.group_dim)
+                    torch.unbind(new_zp, dim=self.group_dim)[:unpadding_shape], dim=self.group_dim)
 
         return new_value, new_scale, new_zp